Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.     

The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia

Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.     

一种结合整型最佳谱间预测与SPIHT的MODIS影像无损压缩算法

针对MODIS(Moderate Resolution Imaging Spectroradiometer)影像数据海量并具有重要研究价值的特点,研究MODIS影像的无损压缩算法。采用最佳线性预测方法,通过波段相关性排序确定波段最优预测顺序,并自适应计算"预测波段"与"当前波段"的二阶最佳预测器系数,减少谱间冗余;以多级树集合分裂树(Set Partitioning In Hierarchical Trees,SPIHT)编码算法降低谱内相关。为确保无损压缩,对线性预测系数进行最佳逼近取整操作,并采用基于提升格式的D5/3整数小波变换。实验结果表明本文提出的算法在压缩比上性能较3DSPIHT等算法突出。     

MODIS数据在广西生态环境监测及评价中的应用方法

利用MODIS数据广西遥感影像,用监督分类法做出了地物覆盖分类图,用主成分分析法做出了土壤侵蚀强度图和生态环境质量综合评价指数图。     

中国MODIS卫星影像彩色镶嵌图制作

本文介绍了大范围卫星影像彩色镶嵌图的制作流程．对于大范围卫星影像彩色镶嵌图制作的技术难点（去云、拼接、调色），文章进行了重点介绍并提出技术解决办法．利用MODIS卫星数据制作了两幅全国彩色影像镶嵌图：一幅为红波段（B1）、绿波段（B4）和蓝波段（B3）构成的真彩色合成影像：一幅为红波段（B1）．近红外波段（B2）和蓝波段（B3）构成的假彩色合成影像．两幅影像色彩精美，信息量丰富．     

卫星遥感在秸秆焚烧监测中的应用

通过对MODIS光谱特征的分析，提出了一种综合运用3S技术进行秸秆焚烧动态监测的资料处理流程和量化判识指标。通过在关中地区2004年麦秸秆焚烧监测中的应用，效果良好，为政府部门迅速、准确地了解全省秸秆焚烧情况，提高预警能力和监督检查的效率提供了科学依据。     

MODIS数据在乌鲁木齐地区植被景观动态监测中的应用

根据EOS／MODIS卫星时次高覆盖面广的特征，选取从4．10月的MODIS晴空资料，对整个乌鲁木齐地区植被景观进行动态监测，利用植被指数最大合成法制作了每月植被指数专题图。为了能够结合物候更好地分析乌鲁木齐地区植被随季节变化趋势以及不同时期植被生长状况，我们还制作了植被景观专题图及其变化评价图。评价图的优势在于能直观地反映植被变化的趋势，并能将变化的程度数字化，还可以根据不同的评价图叠加DEM做相应的分析。为乌鲁木齐地区植被变化研究提供有效的依据。     

MODIS多光谱云相态识别技术的应用研究

介绍了卫星多光谱云相态识别的基本原理, 并给出了EOS/ MODIS云相态识别的流程。针对简单和复杂云场, 利用个例并结合无线电探空资料与MODI S三通道合成图肯定了多光谱云相态识别的合理性和实际效果。文中对几个特定天气系统(西南涡旋和西太平洋台风)下大面积云场相态分析的结果表明:多光谱云相态识别技术有一定应用价值, 且需要引入可见光技术来减少红外谱段对多层云覆盖和薄卷云的相态分析误差。     

基于EOS/MODIS资料的沙尘遥感监测模型研究

在EOS MODIS资料可见光波段光谱特性的深入分析的基础上，提出了一种完全用MODIS可见光波段监测沙尘信息的技术方法。充分利用MODIS资料可见光范围波段划分细致的特点，构造了不同的光谱特征判别函数作为决策树的分支，最后用决策树法成功地对几次沙尘暴过程进行了沙尘信息遥感监测。结果表明，该方法具有良好的效果和实用性。     

遥感数据的遗传-超平面分割算法

在传统的采用超平面作为判别边界的分割方法中，使用统计学的方法确定类判别界面，当区域间的统计值大于区域内的统计值时，就会出现类判决界面难于确定的问题。遗传算法的特点是进行全局优化搜索，利用这种算法优势可以解决传统算法难以解决的超平面空间定位问题，从而解决类判决界面的确定问题。通过EOS／MODIS图像数据的分割对该方法作了详细的介绍，其分类精度明显高于统计学分类方法。